A hybrid machine learning–based multi-objective supervisory control strategy of a full-scale wastewater treatment for cost-effective and sustainable operation under varying influent conditions
Journal of Cleaner Production, ISSN: 0959-6526, Vol: 291, Page: 125853
2021
- 43Citations
- 102Captures
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Article Description
This study develops a multi-objective supervisory control (MOSC) strategy for wastewater treatment based on hybrid machine-learning algorithms that search optimal setpoints of multiple controllers under varying influent conditions. A wastewater treatment plant (WWTP) operation was modeled by Benchmark Simulation Model No. 2 (BSM2), and influent conditions were generated in consideration of a H-WWTP in South Korea. Two proportional-integral (PI) controllers for dissolved oxygen and biogas, and one cascade-PI controller for nitrate were used as local control loops. The MOSC strategy identified five influent scenarios using fuzzy c-means algorithms and nitrogen-to-carbon ratios. Then, the control performance according to influent changes was gauged employing a deep-learning-based approximation model, and optimal setpoints for the controllers were determined by a non-dominated sorting genetic algorithm. The results demonstrate that an intelligent MOSC strategy can identify optimal setpoints to improve WWTP performance and outperform a reference control across a range of possible ratios of total Kjeldahl nitrogen to chemical oxygen demand (TKN/COD) in influent disturbances. The MOSC strategy was also able to accommodate extreme influent conditions, reduce operational costs by 8%, maintain effluent quality, and produce biogas for sustainable WWTP operation.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0959652621000731; http://dx.doi.org/10.1016/j.jclepro.2021.125853; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85100217299&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0959652621000731; https://dx.doi.org/10.1016/j.jclepro.2021.125853
Elsevier BV
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